Details

Autor(en) / Beteiligte

• Kalm, Kristjan
• Norris, Dennis
• van den Berg, Ronald

Titel

Sequence learning recodes cortical representations instead of strengthening initial ones

Ist Teil von

• PLoS computational biology, 2021-05, Vol.17 (5), p.e1008969-e1008969

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• We contrast two computational models of sequence learning. The associative learner posits that learning proceeds by strengthening existing association weights. Alternatively, recoding posits that learning creates new and more efficient representations of the learned sequences. Importantly, both models propose that humans act as optimal learners but capture different statistics of the stimuli in their internal model. Furthermore, these models make dissociable predictions as to how learning changes the neural representation of sequences. We tested these predictions by using fMRI to extract neural activity patterns from the dorsal visual processing stream during a sequence recall task. We observed that only the recoding account can explain the similarity of neural activity patterns, suggesting that participants recode the learned sequences using chunks. We show that associative learning can theoretically store only very limited number of overlapping sequences, such as common in ecological working memory tasks, and hence an efficient learner should recode initial sequence representations.